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AC linkedin-automator

Automate LinkedIn content creation, posting, engagement tracking, and audience growth. Use for posting content, scheduling posts, analyzing engagement metrics, generating content ideas, commenting on posts, and building LinkedIn presence. Requires browser access with LinkedIn logged in.

ClawHub Agent Skills author: zich-dev v1.0.0 9 files · 5 scripts body ≈ 584 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

AnalyzerData and analyticsInfrastructureMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: linkedin-automator (ClawHub)

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 61/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (linkedin-automator) differs from the folder (linkedin-automation)
    • 50Failures and branches. 0 branches, has a failure section
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 26 steps
    • 100Execution cost. Instruction body is 584 tokens
    • 100Running it twice. No mutating operations

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -31 of 5 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 287: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    This LinkedIn skill is mostly a disclosed browser-workflow helper, but it can affect a real public account and its scheduled-post instructions are under-scoped and injection-prone.
    LLM: suspicious (high) · VirusTotal: benign · 10 Sept 2026